[— title: “Research Stuff” output: pdf_document: default html_document: default —
Perguntas!
Quais as perguntas que já foram respondidas?
Como medir a performance de um jogador (e de um grupo de jogadores) na partida
- Como medir a contaminação tóxica de um time de jogadores em uma partida?
- Através do número de reports, assumindo que os reports indicam que um determinado jogador sentiu-se incomodado o suficiente pelo jogador tóxico ao ponto de realizar uma denúncia
- O fato do denunciante ter incluído um texto especificando a motivação de sua denúncia pode significar mais um fator para incuirmos no futuro no calculo da contaminação tóxica.
- Quais os diferentes topicos de conversa utilizados por times de League of legends?
- Tactics, tactics/pos, small talk, arguments, complaints, insults and taunts
- Esses padrões podem ser agrupados em dois grupos: um para tópicos positivos (tactics, tactics/pos, small talk) e outro para tópicos negativos (arguments, complaints, insults e taunts)
- Esses topicos relacionam-se de algum modo com a contaminação tóxica e a performance? Se sim, qual a relação?
- Sim, os tópicos positivos no geral correlacinam-se inversamente com a contaminação, e positivamente com a performance, enquanto tópicos negativos corelacionam-se de maneira positiva com a contaminação, e de maneira negativa com a performance.
- Os tópicos utilizados pelo jogador tóxico influenciam a contaminação de algum modo? Se sim, como?
- Sim, os tópicos negativos utilizados pelo jogador tóxico influenciam a contaminação, afetando os times de maneiras distintas. Enquanto o time aliado é mais disposto a sofrer efeitos negativos de complaints e insultos feitos pelo jogador tóxico, o time inimigo é mais propenso a sofrer efeitos negativos de taunts.
Comparação de jogadores não contaminados com os outros jogadores
Distribuição:
Percentage of non-contaminated players from the total of enemy groups.
nrow(groups[relation.offender=='enemy' & contamination == 0])*100/nrow(groups[relation.offender=='enemy'])## [1] 66.99673Percentage of non-contaminated players from the total of groups.
nrow(groups[relation.offender=='enemy' & contamination == 0])*100/nrow(groups) ## [1] 20.54079Comparação de médias:
Não contaminados vs. contaminados:
Non-contaminated groups also presents higher average performance \((mean=0.120, sd=0.02)\) than contaminated groups \((mean=0.086,sd=0.03)\).
med.t.test(groups[relation.offender=='enemy' & contamination == 0]$performance,
groups[relation.offender!='enemy' | contamination > 0]$performance)## [1] "mean/sd x"
## [1] 0.1204023
## [1] 0.02679578
## [1] "mean/sd y"
## [1] 0.09016112
## [1] 0.04093299##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 1022.3, df = 3090500, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.03018321 0.03029917
## sample estimates:
## mean of x mean of y
## 0.12040231 0.09016112Não Contaminados vs. inimigos contaminados:
Non-contaminated groups also present higher average performance \((mean=0.120, sd=0.02)\) than contaminated \((mean=0.107, sd=0.03)\) enemy groups.
med.t.test(groups[relation.offender=='enemy' & contamination == 0]$performance,
groups[relation.offender=='enemy' & contamination > 0]$performance)## [1] "mean/sd x"
## [1] 0.1204023
## [1] 0.02679578
## [1] "mean/sd y"
## [1] 0.1078593
## [1] 0.0342237##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 258.56, df = 1051300, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01244796 0.01263812
## sample estimates:
## mean of x mean of y
## 0.1204023 0.1078593Não Contaminados vs. Aliados contaminados:
And higher average performance \((mean=0.120, sd=0.02)\) than ally groups \((mean=0.084, sd=0.03)\) as well.
med.t.test(groups[relation.offender=='enemy' & contamination == 0]$performance,
groups[relation.offender=='ally']$performance)## [1] "mean/sd x"
## [1] 0.1204023
## [1] 0.02679578
## [1] "mean/sd y"
## [1] 0.08435106
## [1] 0.03088467##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 1122.4, df = 3069900, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.0359883 0.0361142
## sample estimates:
## mean of x mean of y
## 0.12040231 0.08435106Inimigos Contaminados vs. Aliados (performance):
We can also compare contaminated enemy groups and ally groups, and confirm that contaminated enemy groups \((mean=0.107, sd=0.03)\) have higher average peformance than allies\((mean=0.08, sd=0.03)\).
med.t.test(groups[relation.offender=='enemy' & contamination > 0]$performance,
groups[relation.offender=='ally']$performance)## [1] "mean/sd x"
## [1] 0.1078593
## [1] 0.0342237
## [1] "mean/sd y"
## [1] 0.08435106
## [1] 0.03088467##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 490.9, df = 1018900, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02341435 0.02360207
## sample estimates:
## mean of x mean of y
## 0.10785927 0.08435106med.t.test(groups[relation.offender=='ally' & topic=='complaints']$performance,
groups[relation.offender=='ally' & topic!='complaints']$performance)## [1] "mean/sd x"
## [1] 0.07351362
## [1] 0.02574566
## [1] "mean/sd y"
## [1] 0.08697285
## [1] 0.03143763##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -277.03, df = 687200, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01355445 -0.01336401
## sample estimates:
## mean of x mean of y
## 0.07351362 0.08697285med.t.test(groups[relation offender==‘ally’ & topic==‘complaints’]\(performance, groups[relation.offender=='ally' & topic!='complaints']\)performance)
Inimigos Contaminados vs. Aliados (contaminação):
Finnaly, comparing the contamination index of contaminated enemy groups and ally groups, we see that the contaminated enemy groups \((mean=0.28, sd=0.15)\) still are less contaminated than the allies \((mean=0.30, sd=0.21)\) but with a small difference.
med.t.test(groups[relation.offender=='enemy' & contamination > 0]$contamination,
groups[relation.offender=='ally']$contamination)## [1] "mean/sd x"
## [1] 0.285968
## [1] 0.1556121
## [1] "mean/sd y"
## [1] 0.2999964
## [1] 0.2185822##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -56.476, df = 1549800, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.0145153 -0.0135416
## sample estimates:
## mean of x mean of y
## 0.2859680 0.2999964T-test’s \(p\) is \(2.2*10^{16}\), for all these comparisons.
Distribuição dos tópicos(Positive and Negative):
Por tipo de grupo:
Non-Contaminated:
## arguments chit.chat complaints insults other.langs provoking
## 4.154204 16.267211 5.452007 2.498494 3.092136 1.526151
## tactics tactics.pos
## 24.843725 42.166072## neg pos
## 14.06579 85.93421Contaminated Enemies:
## arguments chit.chat complaints insults other.langs provoking
## 12.377604 17.810885 8.224055 2.540251 2.490208 3.250765
## tactics tactics.pos
## 20.719097 32.587133## neg pos
## 27.06669 72.93331Ally:
## arguments chit.chat complaints insults other.langs provoking
## 15.811375 10.616322 19.521756 5.354951 3.468746 3.163654
## tactics tactics.pos
## 20.808496 21.254699## neg pos
## 45.4275 54.5725Offender:
## arguments chit.chat complaints insults other.langs provoking
## 14.923031 10.530200 27.097166 12.001722 2.774965 7.824406
## tactics tactics.pos
## 15.997512 8.850999## neg pos
## 63.61152 36.38848Por categoria de tópico
Dataset completo:
## arguments chit.chat complaints insults other.langs provoking
## 12.708834 12.485591 18.355101 7.018207 3.025940 4.615350
## tactics tactics.pos
## 19.795542 21.995435Positive Topics:
## arguments chit.chat complaints insults other.langs provoking
## 0.00000 23.00365 0.00000 0.00000 0.00000 0.00000
## tactics tactics.pos
## 36.47162 40.52473Negative Topics:
## arguments chit.chat complaints insults other.langs provoking
## 29.76482 0.00000 42.98871 16.43705 0.00000 10.80942
## tactics tactics.pos
## 0.00000 0.00000Relacionamento entre topicos/grupos e performance/contaminação:
Positive topics:
Correlações:
Performance:
## [1] "Todos:"
## [1] 0.6228213
## [1] "Não Contaminados:"
## [1] 0.8046749
## [1] "Inimigos Contaminados:"
## [1] 0.8432692
## [1] "Aliados:"
## [1] 0.9680895
## [1] "Ofensor:"
## [1] 0.9546973Contaminação:
## [1] "Inimigos:"
## [1] -0.6963913
## [1] "Aliados:"
## [1] -0.7837376Comparisons
## [1] "Full"## [1] "mean/sd x"
## [1] 0.1057205
## [1] 0.03809381
## [1] "mean/sd y"
## [1] 0.0854443
## [1] 0.03992133##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 637.95, df = 5682400, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02021393 0.02033852
## sample estimates:
## mean of x mean of y
## 0.1057205 0.0854443## [1] "Non-Contaminated"## [1] "mean/sd x"
## [1] 0.1224147
## [1] 0.02538513
## [1] "mean/sd y"
## [1] 0.1093002
## [1] 0.03142648##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 167.8, df = 218820, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01296134 0.01326770
## sample estimates:
## mean of x mean of y
## 0.1224147 0.1093002## [1] "Contaminated Enemies"## [1] "mean/sd x"
## [1] 0.1134019
## [1] 0.03214273
## [1] "mean/sd y"
## [1] 0.09398463
## [1] 0.03541024##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 198.06, df = 280720, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01922509 0.01960940
## sample estimates:
## mean of x mean of y
## 0.11340188 0.09398463## [1] "Allies"## [1] "mean/sd x"
## [1] 0.09042173
## [1] 0.03159107
## [1] "mean/sd y"
## [1] 0.07722164
## [1] 0.0284678##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 301.98, df = 1879000, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01311442 0.01328576
## sample estimates:
## mean of x mean of y
## 0.09042173 0.07722164## [1] "Offenders"## [1] "mean/sd x"
## [1] 0.09873421
## [1] 0.05031007
## [1] "mean/sd y"
## [1] 0.08633709
## [1] 0.04514018##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 188.84, df = 1631700, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01226844 0.01252578
## sample estimates:
## mean of x mean of y
## 0.09873421 0.08633709Tactics-related(tactics + tactics.pos):
Correlações:
Performance:
## [1] "Todos:"
## [1] 0.5928543
## [1] "Não Contaminados:"
## [1] 0.7640866
## [1] "Inimigos Contaminados:"
## [1] 0.8192308
## [1] "Aliados:"
## [1] 0.9513276
## [1] "Ofensor:"
## [1] 0.9363036Contaminação:
## [1] "Inimigos:"
## [1] -0.701972
## [1] "Aliados:"
## [1] -0.5905795NhemNhemNhem
## [1] 0.05058955## [1] 0.6081889## [1] 0.4261171Mood-related(tactics.pos + small talk):
Correlações:
Performance:
## [1] "Todos:"
## [1] 0.5935066
## [1] "Não Contaminados:"
## [1] 0.9501938
## [1] "Inimigos Contaminados:"
## [1] 0.95
## [1] "Aliados:"
## [1] 0.9633967
## [1] "Ofensor:"
## [1] 0.6135424Contamination:
## [1] "Inimigos:"
## [1] -0.6732689
## [1] "Aliados:"
## [1] -0.8073669Comparações:
Performance:
Non-contaminated:
## [1] "mean/sd x"
## [1] 0.1242648
## [1] 0.02529473
## [1] "mean/sd y"
## [1] 0.1149968
## [1] 0.02785384##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 195.14, df = 1103700, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.009174971 0.009361142
## sample estimates:
## mean of x mean of y
## 0.1242648 0.1149968Contaminated enemy:
## [1] "mean/sd x"
## [1] 0.1144905
## [1] 0.03257593
## [1] "mean/sd y"
## [1] 0.1013424
## [1] 0.03450893##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 157.35, df = 641980, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01298425 0.01331180
## sample estimates:
## mean of x mean of y
## 0.1144905 0.1013424Ally:
## [1] "mean/sd x"
## [1] 0.09301305
## [1] 0.03316181
## [1] "mean/sd y"
## [1] 0.0802906
## [1] 0.02886421##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 260.46, df = 1081600, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01262671 0.01281818
## sample estimates:
## mean of x mean of y
## 0.09301305 0.08029060Offender:
## [1] "mean/sd x"
## [1] 0.1007124
## [1] 0.05380595
## [1] "mean/sd y"
## [1] 0.08834091
## [1] 0.04551635##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 145.58, df = 640900, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01220493 0.01253804
## sample estimates:
## mean of x mean of y
## 0.10071240 0.08834091Negative Topics:
Correlações:
## [1] "Todos:"
## [1] -0.5358722
## [1] "Não Contaminados:"
## [1] -0.8778429
## [1] "Inimigos Contaminados:"
## [1] -0.916787
## [1] "Aliados:"
## [1] -0.9518999
## [1] "Ofensor:"
## [1] -0.3573428## [1] "For performance < 0.06:"## [1] 0.8339901## [1] "For performance >= 0.06:"## [1] -0.8853053Comparisons:
Comparisons
## [1] "Full"## [1] "mean/sd x"
## [1] 0.0854443
## [1] 0.03992133
## [1] "mean/sd y"
## [1] 0.1057205
## [1] 0.03809381##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -637.95, df = 5682400, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.02033852 -0.02021393
## sample estimates:
## mean of x mean of y
## 0.0854443 0.1057205## [1] "Non-Contaminated"## [1] "mean/sd x"
## [1] 0.1093002
## [1] 0.03142648
## [1] "mean/sd y"
## [1] 0.1224147
## [1] 0.02538513##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -167.8, df = 218820, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01326770 -0.01296134
## sample estimates:
## mean of x mean of y
## 0.1093002 0.1224147## [1] "Contaminated Enemies"## [1] "mean/sd x"
## [1] 0.09398463
## [1] 0.03541024
## [1] "mean/sd y"
## [1] 0.1134019
## [1] 0.03214273##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -198.06, df = 280720, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01960940 -0.01922509
## sample estimates:
## mean of x mean of y
## 0.09398463 0.11340188## [1] "Allies"## [1] "mean/sd x"
## [1] 0.07722164
## [1] 0.0284678
## [1] "mean/sd y"
## [1] 0.09042173
## [1] 0.03159107##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -301.98, df = 1879000, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01328576 -0.01311442
## sample estimates:
## mean of x mean of y
## 0.07722164 0.09042173## [1] "Offenders"## [1] "mean/sd x"
## [1] 0.08633709
## [1] 0.04514018
## [1] "mean/sd y"
## [1] 0.09873421
## [1] 0.05031007##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -188.84, df = 1631700, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01252578 -0.01226844
## sample estimates:
## mean of x mean of y
## 0.08633709 0.09873421Arguments:
Correlações:
Performance:
## [1] "Todos:"
## [1] -0.6423828
## [1] "Não Contaminados:"
## [1] -0.4298965
## [1] "Inimigos Contaminados:"
## [1] -0.6259034
## [1] "Aliados:"
## [1] -0.9224592
## [1] "Ofensor:"
## [1] -0.6907605Contaminação
## [1] "Todos:"
## [1] -0.6423828
## [1] "Não Contaminados:"
## [1] -0.4298965
## [1] "Inimigos Contaminados:"
## [1] -0.6259034
## [1] "Aliados:"
## [1] -0.9224592
## [1] "Ofensor:"
## [1] -0.6907605Comparisons:
Performance:
Non-contaminated:
## [1] "mean/sd x"
## [1] 0.1135278
## [1] 0.03188396
## [1] "mean/sd y"
## [1] 0.1207108
## [1] 0.02649725##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -51.843, df = 57852, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.007454559 -0.006911430
## sample estimates:
## mean of x mean of y
## 0.1135278 0.1207108Contaminated enemy:
## [1] "mean/sd x"
## [1] 0.09761533
## [1] 0.03675039
## [1] "mean/sd y"
## [1] 0.1094313
## [1] 0.03355229##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -85.958, df = 99616, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01208542 -0.01154657
## sample estimates:
## mean of x mean of y
## 0.09761533 0.10943133Ally:
## [1] "mean/sd x"
## [1] 0.0778424
## [1] 0.02943269
## [1] "mean/sd y"
## [1] 0.08556669
## [1] 0.03098617##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -132.87, df = 448410, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.007838233 -0.007610358
## sample estimates:
## mean of x mean of y
## 0.07784240 0.08556669Offender:
## [1] "mean/sd x"
## [1] 0.08624908
## [1] 0.04867387
## [1] "mean/sd y"
## [1] 0.09152615
## [1] 0.04723439##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -60.413, df = 488860, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.005448276 -0.005105872
## sample estimates:
## mean of x mean of y
## 0.08624908 0.09152615## arguments chit.chat complaints insults other.langs provoking
## 0.2976482 0.0000000 0.4298871 0.1643705 0.0000000 0.1080942
## tactics tactics.pos
## 0.0000000 0.0000000Minimum for non-contaminated and contaminated enemies.
## [1] "For non-contaminated"## [1] 0.1262249## [1] 0.4280555## [1] "For contaminated enemies"## [1] 0.1311179## [1] 0.6017316## [1] "For non-contaminated"## [1] 0.1262249## [1] -0.8223408## [1] "For contaminated enemies"## [1] 0.1311179## [1] -0.9351802## [1] "mean/sd x"
## [1] 0.2871684
## [1] 0.1527032
## [1] "mean/sd y"
## [1] 0.285968
## [1] 0.1556121##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 3.1332, df = 369350, p-value = 0.001729
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.0004495086 0.0019513567
## sample estimates:
## mean of x mean of y
## 0.2871684 0.2859680## [1] "mean/sd x"
## [1] 0.05502653
## [1] 0.01003389
## [1] "mean/sd y"
## [1] 0.14408
## [1] 0.008394818##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -5950, df = 1481900, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08908280 -0.08902413
## sample estimates:
## mean of x mean of y
## 0.05502653 0.14408000Complaints:
Offender’s peak:
## [1] 0.0559267## [1] 0.8739402## [1] -0.9259598Correlações:
## [1] "Todos:"
## [1] -0.6048952
## [1] "Não Contaminados:"
## [1] -0.8843088
## [1] "Inimigos Contaminados:"
## [1] -0.9066414
## [1] "Aliados:"
## [1] -0.9591255
## [1] "Ofensor:"
## [1] -0.5048056## [1] "Inimigos:"
## [1] 0.6241439
## [1] "Aliados:"
## [1] 0.7716947Comparisons:
Non-contaminated:
## [1] "mean/sd x"
## [1] 0.1041836
## [1] 0.03166442
## [1] "mean/sd y"
## [1] 0.1213482
## [1] 0.02616682##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -142.26, df = 77320, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01740105 -0.01692806
## sample estimates:
## mean of x mean of y
## 0.1041836 0.1213482Contaminated enemies:
## [1] "mean/sd x"
## [1] 0.08755252
## [1] 0.03373041
## [1] "mean/sd y"
## [1] 0.1097983
## [1] 0.03362745##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -145.6, df = 62913, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.02254523 -0.02194632
## sample estimates:
## mean of x mean of y
## 0.08755252 0.10979830Allies:
## [1] "mean/sd x"
## [1] 0.07351362
## [1] 0.02574566
## [1] "mean/sd y"
## [1] 0.08697285
## [1] 0.03143763##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -277.03, df = 687200, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01355445 -0.01336401
## sample estimates:
## mean of x mean of y
## 0.07351362 0.08697285Offenders:
## [1] "mean/sd x"
## [1] 0.08290308
## [1] 0.04166307
## [1] "mean/sd y"
## [1] 0.09365105
## [1] 0.04916283##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -169.73, df = 1375900, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01087208 -0.01062386
## sample estimates:
## mean of x mean of y
## 0.08290308 0.09365105Comparisons in negative topics:
Insults:
####Correlações: Performance:
## [1] "Todos:"
## [1] -0.3951109
## [1] "Não Contaminados:"
## [1] -0.8234762
## [1] "Inimigos Contaminados:"
## [1] -0.825512
## [1] "Aliados:"
## [1] -0.4388466
## [1] "Ofensor:"
## [1] 0.3087258Contamination:
## [1] "Inimigos:"
## [1] 0.001536159
## [1] "Aliados:"
## [1] 0.1302296Offender’s peak
## [1] 0.0910003## [1] 0.8134174## [1] -0.8080494Comparisons:
Non-contaminated:
## [1] "mean/sd x"
## [1] 0.1080204
## [1] 0.02999186
## [1] "mean/sd y"
## [1] 0.1207299
## [1] 0.02661796##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -75.988, df = 34139, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01303736 -0.01238170
## sample estimates:
## mean of x mean of y
## 0.1080204 0.1207299Contaminated enemies:
## [1] "mean/sd x"
## [1] 0.09311682
## [1] 0.03321781
## [1] "mean/sd y"
## [1] 0.1083559
## [1] 0.03412522##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -57.951, df = 17309, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01575451 -0.01472365
## sample estimates:
## mean of x mean of y
## 0.09311682 0.10835590Allies:
## [1] "mean/sd x"
## [1] 0.07997617
## [1] 0.02802309
## [1] "mean/sd y"
## [1] 0.08459258
## [1] 0.03100976##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -51.595, df = 119890, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.004791779 -0.004441045
## sample estimates:
## mean of x mean of y
## 0.07997617 0.08459258Offenders:
## [1] "mean/sd x"
## [1] 0.08290308
## [1] 0.04166307
## [1] "mean/sd y"
## [1] 0.09365105
## [1] 0.04916283##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -169.73, df = 1375900, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01087208 -0.01062386
## sample estimates:
## mean of x mean of y
## 0.08290308 0.09365105Taunts:
Correlações:
Performance:
## [1] "Todos:"
## [1] -0.2348258
## [1] "Não Contaminados:"
## [1] -0.4600085
## [1] "Inimigos Contaminados:"
## [1] -0.8124259
## [1] "Aliados:"
## [1] 0.5774775
## [1] "Ofensor:"
## [1] 0.3243221Contaminação:
## [1] "Inimigos:"
## [1] 0.7040565
## [1] "Aliados:"
## [1] -0.4560991Comparisons:
Non-contaminated:
## [1] "mean/sd x"
## [1] 0.1181662
## [1] 0.02785825
## [1] "mean/sd y"
## [1] 0.1204472
## [1] 0.0267625##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -11.508, df = 20613, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.002669483 -0.001892476
## sample estimates:
## mean of x mean of y
## 0.1181662 0.1204472Contaminated enemy:
## [1] "mean/sd x"
## [1] 0.09711102
## [1] 0.0336455
## [1] "mean/sd y"
## [1] 0.1083336
## [1] 0.03414479##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -47.501, df = 22457, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01168568 -0.01075951
## sample estimates:
## mean of x mean of y
## 0.09711102 0.10833361Ally:
## [1] "mean/sd x"
## [1] 0.0923376
## [1] 0.03400452
## [1] "mean/sd y"
## [1] 0.08408427
## [1] 0.03073199##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 59.629, df = 65317, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.007982049 0.008524621
## sample estimates:
## mean of x mean of y
## 0.09233760 0.08408427Offender:
## [1] "mean/sd x"
## [1] 0.09487084
## [1] 0.05008511
## [1] "mean/sd y"
## [1] 0.09038789
## [1] 0.04724566##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 37.59, df = 219330, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.004249206 0.004716695
## sample estimates:
## mean of x mean of y
## 0.09487084 0.09038789## arguments chit.chat complaints insults other.langs provoking
## 0.2976482 0.0000000 0.4298871 0.1643705 0.0000000 0.1080942
## tactics tactics.pos
## 0.0000000 0.0000000Conclusions(Positive):
Complaints & Insults : Offenders with low performance tends to complain more, and complain about his own team. Complaints:
#summary(groups$performance)
#summary(offender.topics.perf[performance > 0.094]$complaints)
#summary(offender.topics.perf[performance <= 0.094]$complaints)
summary(matches$offender.team)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.3607 0.7729 0.9696 1.3460 674.1000summary(matches[offender.performance > 0.094 | offender.groups!='complaints']$offender.team)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.3510 0.7412 0.9419 1.2980 674.1000summary(matches[offender.performance <= 0.094 & offender.groups=='complaints']$offender.team)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.5273 0.9681 1.1560 1.5670 34.4100Enemy teams taunts more on high contamination and low performance as a response to the opposing team taunts.
Comparison betweeen the mean contamination of each topic: General: Positive Topics:
'Positive:'## [1] "Positive:" mean(groups[topic.2=='pos']$contamination,na.rm=TRUE)## [1] 0.1573973 'Tactics:'## [1] "Tactics:" mean(groups[topic.2=='pos' & (topic=='tactics')]$contamination,na.rm=TRUE)## [1] 0.1868681 'Tactics.pos:'## [1] "Tactics.pos:" mean(groups[topic.2=='pos' & (topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.1375642 'Small Talk:'## [1] "Small Talk:" mean(groups[topic.2=='pos' & (topic=='chit.chat')]$contamination,na.rm=TRUE)## [1] 0.1533754 'Tactics.all:'## [1] "Tactics.all:" mean(groups[topic.2=='pos' & (topic=='tactics' | topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.1584509 'Mood:'## [1] "Mood:" mean(groups[topic.2=='pos' & (topic=='chit.chat' | topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.1425052Positive Topics [Ally]:
'Positive:'## [1] "Positive:" mean(groups[relation.offender=='ally' & topic.2=='pos']$contamination,na.rm=TRUE)## [1] 0.2718455 'Tactics:'## [1] "Tactics:" mean(groups[relation.offender=='ally' & topic.2=='pos' & (topic=='tactics')]$contamination,na.rm=TRUE)## [1] 0.3099444 'Tactics.pos:'## [1] "Tactics.pos:" mean(groups[relation.offender=='ally' & topic.2=='pos' & (topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.2527472 'Small Talk:'## [1] "Small Talk:" mean(groups[relation.offender=='ally' & topic.2=='pos' & (topic=='chit.chat')]$contamination,na.rm=TRUE)## [1] 0.2354061 'Tactics.all:'## [1] "Tactics.all:" mean(groups[relation.offender=='ally' & topic.2=='pos' & (topic=='tactics' | topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.2810425 'Mood:'## [1] "Mood:" mean(groups[relation.offender=='ally' & topic.2=='pos' & (topic=='chit.chat' | topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.2469709Positive Topics[Non Contaminated Enemies]:
'Positive:'## [1] "Positive:" mean(groups[relation.offender=='enemy' & topic.2=='pos']$contamination,na.rm=TRUE)## [1] 0.08132393 'Tactics:'## [1] "Tactics:" mean(groups[relation.offender=='enemy' & topic.2=='pos' & (topic=='tactics')]$contamination,na.rm=TRUE)## [1] 0.07779355 'Tactics.pos:'## [1] "Tactics.pos:" mean(groups[relation.offender=='enemy' & topic.2=='pos' & (topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.07479239 'Small Talk:'## [1] "Small Talk:" mean(groups[relation.offender=='enemy' & topic.2=='pos' & (topic=='chit.chat')]$contamination,na.rm=TRUE)## [1] 0.1014537 'Tactics.all:'## [1] "Tactics.all:" mean(groups[relation.offender=='enemy' & topic.2=='pos' & (topic=='tactics' | topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.0759202 'Mood:'## [1] "Mood:" mean(groups[relation.offender=='enemy' & topic.2=='pos' & (topic=='chit.chat' | topic=='tactics.pos')]$contamination,na.rm=TRUE)## [1] 0.08281015Negative Topics
'Negative:'## [1] "Negative:" mean(groups[topic.2=='neg']$contamination,na.rm=TRUE)## [1] 0.2819183Relacionar contaminação com a presença do ofensor no ‘all chat’
med.t.test(matches[offender.all == 0]$enemy.contamination, matches[offender.all > 0]$enemy.contamination)## [1] "mean/sd x"
## [1] 0.05000988
## [1] 0.1158038
## [1] "mean/sd y"
## [1] 0.1083691
## [1] 0.171027##
## Welch Two Sample t-test
##
## data: x and not_x
## t = -266.14, df = 1166800, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.05878901 -0.05792944
## sample estimates:
## mean of x mean of y
## 0.05000988 0.10836911med.t.test(matches[offender.all == 0]$ally.contamination, matches[offender.all > 0]$ally.contamination)## [1] "mean/sd x"
## [1] 0.3180026
## [1] 0.195484
## [1] "mean/sd y"
## [1] 0.2943188
## [1] 0.2250766##
## Welch Two Sample t-test
##
## data: x and not_x
## t = 69.803, df = 897030, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02301881 0.02434882
## sample estimates:
## mean of x mean of y
## 0.3180026 0.2943188Non-contaminated groups dominate the 25% high performance scores.
x = summary(groups$performance)
full = nrow(groups[performance >= x[5]])
part = nrow(groups[performance >= x[5] & relation.offender=='enemy' & contamination == 0])
print("Non-contaminated")## [1] "Non-contaminated" print(part/full)## [1] 0.4128436 part = nrow(groups[performance >= x[5] & relation.offender=='enemy' & contamination > 0])
print("Contaminated enemies")## [1] "Contaminated enemies" print(part/full)## [1] 0.1562736 part = nrow(groups[performance >= x[5] & relation.offender=='ally'])
print("Allies")## [1] "Allies" print(part/full)## [1] 0.1550557 print("Offenders")## [1] "Offenders" part = nrow(groups[performance >= x[5] & relation.offender=='offender'])
print(part/full)## [1] 0.275827Alvos do ofensor em certos tópicos negativos
Presença do ofensor no ‘all’ chat aumenta contaminação no time inimigo
summary(matches[offender.all==0]$enemy.contamination)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05001 0.00000 1.00000summary(matches[offender.all >0]$enemy.contamination)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1084 0.2000 1.0000part=nrow(matches[offender.all==0])
full=nrow(matches)
part/full## [1] 0.2397275Insults
Podemos ver que a média da atividade do ofensor no canal ‘all’ quando este predominantemente insulta \((mean=0.20)\) é menor do que a média de atividade na predominância de outros tópicos \((mean=0.32)\). Isso mostra que o ofensor dá menos atenção ao grupo inimigo em casos de insulto. Não temos confirmação de que o ofensor foca seus ataques no grupo aliado através da atividade no chat. Contudo, analisando as palavras utilizadas pelo ofensor e a relação entre a quantidade de insultos realizadas pelo ofensor e a contaminação, fica claro que o time aliado é consideravelmente afetado por estes insultos.
x <- matches[offender.groups=='insults']$offender.all
not.x <- matches[offender.groups!='insults']$offender.all
t.test(x,not.x)##
## Welch Two Sample t-test
##
## data: x and not.x
## t = -115.85, df = 432660, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1237800 -0.1196616
## sample estimates:
## mean of x mean of y
## 0.1993550 0.3210758x <- matches[offender.groups=='insults']$offender.team
not.x <- matches[offender.groups!='insults']$offender.team
t.test(x,not.x)##
## Welch Two Sample t-test
##
## data: x and not.x
## t = -58.656, df = 372260, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1136733 -0.1063222
## sample estimates:
## mean of x mean of y
## 0.8958369 1.0058346Taunts
A média da atividade do ofensor no canal ‘all’ quando este predominantemente provoca \((mean=0.45)\), é significativamente superior \((p < 2.2*10^{16})\) a média da atividade do ofensor no ‘all’ quando outros tópicos são predominantes \((mean=0.29)\). isso mostra que em casos de provocação, o ofensor foca no time inimigo em detrimento do time aliado, que mostra atividade significativamente mais baixa \((p < 2.2*10^{16})\) por parte do ofensor em casos onde provocação é predominante \((mean=0.68)\) do que em outros casos \((mean=1.01)\).
x <- matches[offender.groups=='provoking']$offender.all
not.x <- matches[offender.groups!='provoking']$offender.all
t.test(x,not.x)##
## Welch Two Sample t-test
##
## data: x and not.x
## t = 35.775, df = 140500, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1526535 0.1703497
## sample estimates:
## mean of x mean of y
## 0.4559313 0.2944297x <- matches[offender.groups=='provoking']$offender.team
not.x <- matches[offender.groups!='provoking']$offender.team
t.test(x,not.x)##
## Welch Two Sample t-test
##
## data: x and not.x
## t = -53.937, df = 141710, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3486237 -0.3241753
## sample estimates:
## mean of x mean of y
## 0.6801412 1.0165407Podemos replicar esse experimento para os tópicos negativos restantes:
Complaints
Diferença estatisticamente significativa em ambos, logo, consideramos que houve um ‘empate técnico’. Contudo, a diferença real no ‘team’ é bem significativa \(mean=1.11\) para grupos com complaints, \(mean=0.95\) para grupos sem complaints. Isso significa que ofensores que reclamam alvejam o time aliado mais do que o normal.
x <- matches[offender.groups=='complaints']$offender.all
not.x <- matches[offender.groups!='complaints']$offender.all
t.test(x,not.x)##
## Welch Two Sample t-test
##
## data: x and not.x
## t = 6.5847, df = 1575600, p-value = 4.558e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.004174234 0.007712285
## sample estimates:
## mean of x mean of y
## 0.3103061 0.3043628x <- matches[offender.groups=='complaints']$offender.team
not.x <- matches[offender.groups!='complaints']$offender.team
t.test(x,not.x)##
## Welch Two Sample t-test
##
## data: x and not.x
## t = 106.71, df = 1313700, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1608507 0.1668698
## sample estimates:
## mean of x mean of y
## 1.1088832 0.9450229# plt <- plot.topic.perf('neg') + labs(x='Group Performance',y='Groups w/ Negative Topics (%)')
# save.plot('negative_perf.png',plt,w=6)
# plt <- plot.topic.perf('arguments') + labs(x='Group Performance',y='Groups w/ Argument Topics (%)')
# save.plot('arguments_perf.png',plt,w=6)
# plt <- plot.topic.perf('complaints') + labs(x='Group Performance',y='Groups w/ Complaint Topics (%)')
# save.plot('complaints_perf.png',plt,w=6)
# plt <- plot.topic.perf('insults') + labs(x='Group Performance',y='Groups w/ Insult Topics (%)')
# save.plot('insults_perf.png',plt,w=6)
# plt <- plot.topic.perf('provoking') + labs(x='Group Performance',y='Groups w/ Taunts Topics (%)')
# save.plot('taunts_perf.png',plt,w=6)
# plt <- plot.topic.cont('neg') + labs(x="Group Contamination", y='Groups w/ Negative Topics (%)')
# save.plot('negative_cont.png',plt)
# plt <- plot.topic.cont('arguments') + labs(x="Group Contamination", y='Groups w/ Argument Topics (%)')
# save.plot('arguments_cont.png',plt)
# plt <- plot.topic.cont('complaints') + labs(x="Group Contamination", y='Groups w/ Complaint Topics (%)')
# save.plot('complaints_cont.png',plt)
# plt <- plot.topic.cont('insults') + labs(x="Group Contamination", y='Groups w/ Insult Topics (%)')
# save.plot('insults_cont.png',plt)
# plt <- plot.topic.cont('provoking') + labs(x="Group Contamination", y='Groups w/ Taunt Topics (%)')
# save.plot('provoking_cont.png',plt)Tabela de Outcomes(Passar para um arquivo .R eventualmente)
Adicionando ally e enemy targets a matches(Passar para o groups.R eventualmente)
Insults and Taunts - Some negative topics does not affect its own team.
summary(matches[ally.groups=='provoking' & ally.chat.all > 0 & ally.chat.team == 0]$enemy.contamination)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2000 0.4000 0.3844 0.6000 1.0000summary(matches[ally.groups=='insults' & ally.chat.all > 0 & ally.chat.team == 0]$enemy.contamination)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.2000 0.2927 0.5000 1.0000summary(matches[enemy.groups=='insults' & enemy.chat.all > 0 & enemy.chat.team == 0]$ally.contamination)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1435 0.2500 1.0000summary(matches[enemy.groups=='insults' & !(enemy.chat.all > 0 & enemy.chat.team == 0)]$enemy.contamination)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08177 0.20000 1.00000Offender Topics
Negative Topics
###Correlations Performance:
offender.correlations.perf(column)## [1] "Não Contaminados:"
## [1] 0.7885939
## [1] "Inimigos Contaminados:"
## [1] 0.8625
## [1] "Aliados:"
## [1] -0.9147327Contamination:
offender.correlations.cont(column)## [1] "Inimigos:"
## [1] -0.4042116
## [1] "Aliados:"
## [1] 0.6735054Complaints
###Correlations Performance:
offender.correlations.perf(column)## [1] "Não Contaminados:"
## [1] 0.8152262
## [1] "Inimigos Contaminados:"
## [1] 0.8958885
## [1] "Aliados:"
## [1] -0.9231925Contamination:
offender.correlations.cont(column)## [1] "Inimigos:"
## [1] -0.6210242
## [1] "Aliados:"
## [1] 0.6936982Arguments
###Correlations Performance:
offender.correlations.perf(column)## [1] "Não Contaminados:"
## [1] 0.5768569
## [1] "Inimigos Contaminados:"
## [1] 0.7022608
## [1] "Aliados:"
## [1] -0.4806165Contamination:
offender.correlations.cont(column)## [1] "Inimigos:"
## [1] 0.5244392
## [1] "Aliados:"
## [1] 0.3898706Insults
###Correlations Performance:
offender.correlations.perf(column)## [1] "Não Contaminados:"
## [1] -0.5121617
## [1] "Inimigos Contaminados:"
## [1] 0.404235
## [1] "Aliados:"
## [1] -0.2365162Contamination:
offender.correlations.cont(column)## [1] "Inimigos:"
## [1] -0.6239175
## [1] "Aliados:"
## [1] 0.4408136Taunts
Correlations
Performance:
offender.correlations.perf(column)## [1] "Não Contaminados:"
## [1] -0.4894697
## [1] "Inimigos Contaminados:"
## [1] -0.684642
## [1] "Aliados:"
## [1] 0.7340415Contamination:
offender.correlations.cont(column)## [1] "Inimigos:"
## [1] 0.7096013
## [1] "Aliados:"
## [1] -0.34537KDA X Performance
KDA:
Central 50%:
prop.table(summary(players[kda > x[2] & kda < x[5] & outcome != 'Leave']$outcome))## Leave Loss Win
## 0.0000000 0.5095367 0.4904633Lower 25%:
prop.table(summary(players[kda <= x[2] & outcome != 'Leave']$outcome))## Leave Loss Win
## 0.00000000 0.90857458 0.09142542Upper 25%:
prop.table(summary(players[kda >= x[5] & outcome != 'Leave']$outcome))## Leave Loss Win
## 0.00000000 0.08091766 0.91908234Means: KDA full summary:
summary(players$kda)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.083 2.000 2.720 3.400 88.000Winners kda summary:
summary(players[outcome=='Win']$kda)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.200 3.250 4.076 5.000 88.000Losers kda summary:
summary(players[outcome=='Loss']$kda)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.700 1.200 1.401 1.857 66.000Performance:
Central 50%:
prop.table(summary(players[performance > x[2] & performance < x[5] & outcome != 'Leave']$outcome))## Leave Loss Win
## 0.0000000 0.4930341 0.5069659Lower 25%:
prop.table(summary(players[performance <= x[2] & outcome != 'Leave']$outcome))## Leave Loss Win
## 0.00000000 0.93510478 0.06489522Upper 25%:
prop.table(summary(players[performance >= x[5] & outcome != 'Leave']$outcome))## Leave Loss Win
## 0.00000000 0.08053301 0.91946699Means: Performance full summary:
summary(players$performance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01368 0.06456 0.09013 0.10000 0.12340 0.61000Winners performance summary:
summary(players[outcome=='Win']$performance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01751 0.09574 0.11940 0.12980 0.15210 0.61000Losers performance summary:
summary(players[outcome=='Loss']$performance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01442 0.05271 0.06720 0.07140 0.08522 0.49810Comparison
Group performance:
Central 50%:
prop.table(summary(groups[performance > x[2] & performance < x[5] & outcome != 'Leave' & relation.offender != 'offender']$outcome))## Loss Win
## 0.4989203 0.5010797Lower 25%:
prop.table(summary(groups[relation.offender != 'offender' & performance <= x[2] & outcome != 'Leave']$outcome))## Loss Win
## 0.997567401 0.002432599Upper 25%:
prop.table(summary(groups[relation.offender != 'offender' & performance >= x[5] & outcome != 'Leave']$outcome))## Loss Win
## 0.003679112 0.996320888Means: Performance full summary:
summary(groups[relation.offender != 'offender']$performance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01994 0.06965 0.10010 0.10030 0.13080 0.20290Winners performance summary:
summary(groups[relation.offender != 'offender' & outcome=='Win']$performance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0325 0.1168 0.1307 0.1294 0.1432 0.2029Losers performance summary:
summary(groups[relation.offender != 'offender' & outcome=='Loss']$performance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01994 0.05752 0.06971 0.07122 0.08336 0.17820